Real-time on-board image processing using an embedded GPU for monocular vision-based navigation

Abstract

In this work we present a new image-based navigation method for guiding a mobile robot equipped only with a monocular camera through a naturally delimited path. The method is based on segmenting the image and classifying each super-pixel to infer a contour of navigable space. While image segmentation is a costly computation, in this case we use a low-power embedded GPU to obtain the necessary framerate in order to achieve a reactive control for the robot. Starting from an existing GPU implementation of the quick-shift segmentation algorithm, we introduce some simple optimizations which result in a speedup which makes real-time processing on board a mobile robot possible. Performed experiments using both a dataset of images and an online on-board execution of the system in an outdoor environment demonstrate the validity of this approach. © 2012 Springer-Verlag.